System, apparatus and method for energy management, for usage by consumers of energy from electric utility service providers, and monitoring and management of same

ABSTRACT

The disclosure includes methods, systems and apparatus for predictive management of efficient selecting and receiving of retail electric utility service to a facility for a period, by automated selecting of a retail utility service provider corresponding to a selected least cost path of predicted rate plan choices across the period, wherein costs of all possible, viable time-bounded predicted rate plan choices are determined for predicted consumer usage where a predicted market of retail rate formulas for the period are predicted in relation to at least one variable, such as weather.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority under 35 U.S.C. § 119 to U.S.Provisional Patent Appl. Ser. No. 62/036,019 entitled “METHODS ANDSYSTEMS FOR CONSUMER-CENTRIC ENERGY MANAGEMENT” filed in the name ofDavis II et al. on Aug. 11, 2014, the entirety of which is incorporatedherein by reference. This application claims priority under 35 U.S.C. §119 to U.S. Non-provisional patent application Ser. No. 14/880,268entitled “SYSTEM, APPARATUS AND METHOD FOR ENERGY MANAGEMENT, FOR USAGEBY CONSUMERS OF ENERGY FROM ELECTRIC UTILITY SERVICE PROVIDERS, ANDMONITORING AND MANAGEMENT OF SAME” filed in the name of Michael AndrewDavis II et al. on Oct. 11, 2015, the entirety of which is incorporatedherein by reference. This application claims priority under 35 U.S.C. §119 to U.S. Non-provisional patent application Ser. No. 16/008,778entitled “SYSTEM, APPARATUS AND METHOD FOR ENERGY MANAGEMENT, FOR USAGEBY CONSUMERS OF ENERGY FROM ELECTRIC UTILITY SERVICE PROVIDERS, ANDMONITORING AND MANAGEMENT OF SAME” filed in the name of Michael AndrewDavis II et al. on Jun. 14, 2018, the entirety of which is incorporatedherein by reference. This application claims priority under 35 U.S.C. §119 to U.S. Non-provisional patent application Ser. No. 16/037,225entitled “SYSTEM, APPARATUS AND METHOD FOR ENERGY MANAGEMENT, FOR USAGEBY CONSUMERS OF ENERGY FROM ELECTRIC UTILITY SERVICE PROVIDERS, ANDMONITORING AND MANAGEMENT OF SAME” filed in the name of Michael AndrewDavis II et al. on Jul. 17, 2018, the entirety of which is incorporatedherein by reference. This application claims priority under 35 U.S.C. §119 to U.S. Non-provisional patent application Ser. No. 17/077,527entitled “SYSTEM, APPARATUS AND METHOD FOR ENERGY MANAGEMENT, FOR USAGEBY CONSUMERS OF ENERGY FROM ELECTRIC UTILITY SERVICE PROVIDERS, ANDMONITORING AND MANAGEMENT OF SAME” filed in the name of Michael AndrewDavis II et al. on Oct. 22, 2020, the entirety of which is incorporatedherein by reference.

FIELD OF THE INVENTION

This disclosure generally relates to systems, apparatus and methods forenergy management. Disclosed subject matter relates to systems,apparatus and methods for usage by consumers of energy from electric andother utility service providers. Disclosed subject matter relates tosystems, apparatus and methods for monitoring and management of usage byconsumers of energy from electric utility service providers.

BACKGROUND OF THE INVENTION

The energy industry is seeing increasing deployment of smart devices andis being impacted by the Internet of Things (IoT), increased energyefficiency, Distributed Energy Resources (DER), and deregulation of theenergy markets and electric utility service providers. A number ofstates have either already deregulated or are on the path toderegulating energy markets. There is need for improved systems,apparatus and methods for energy management.

BRIEF SUMMARY OF THE INVENTION

Disclosed subject matter provides improved systems, apparatus andmethods for energy management. Disclosed subject matter providesimproved systems, apparatus and methods for usage by consumers of energyfrom electric utility service providers. Disclosed subject matterprovides improved systems, apparatus and methods for monitoring andmanagement of usage by consumers of energy from electric utility serviceproviders.

Disclosed subject matter improves consumer usage of energy, such asenergy from electric utility service providers. Disclosed subject matterimproves a consumer's monitoring and management of energy usage, such asenergy usage from electric utility service providers. Disclosed subjectmatter may improve consumer usage of energy, such as energy fromelectric utility service providers in deregulated, regulated, andtransitioning energy markets. Disclosed subject matter may improve aconsumer's monitoring and management of energy usage, such as energyusage from electric utility service providers in deregulated, regulated,and transitioning energy markets. Disclosed subject matter may simplifythe selecting, managing and consuming or usage of energy by consumersfrom electric utility service providers by providing independent,simplified, favorable and unbiased processes for monitoring andmanagement of bills for usage or consumption of energy by consumers fromelectric utility service providers. Disclosed subject matter may, forexample, bridge gaps between Local Energy Providers (LEP) and consumersin deregulated markets. Embodiments of disclosed subject matter mayprovide an improved single source for obtaining unbiased informationregarding utility usage and service providers, including automatedmanagement of contracting with utility service providers, billing fromutility service providers, and monitoring and management of energyusage.

Disclosed subject matter provides improved systems, apparatus andmethods for usage, monitoring and management of electric energy suppliedto consumers from electric utility service providers in deregulated,partially deregulated and regulated electric energy markets. As usedherein, “consumer” means and includes a customer, account, energyconsuming entity, or consumer of electric energy from an electricutility service provider. As used herein, “electric utility serviceprovider” means and includes any provider or third party that provides,sells, supplies, markets, sets prices, or contracts for the providing ofelectric energy or electric utility service. Embodiments may provideimproved systems, apparatus and methods for consumers having access tosmart meter or related technology infrastructure. Embodiments mayprovide improved systems, apparatus and methods for electric utilityservice providers to monitor and manage energy usage by consumers,relationships with consumers, accounts of consumers, and may provide formanagement to reduce energy usage, such as peak energy usage, by eachconsumer, or cumulatively across a population of consumers. Embodimentsmay provide improved systems, apparatus and methods for consolidatedmanagement and monitoring of usage for all utility services, including,e.g. electric utility service, gas utility service, and water utilityservice. Embodiments may provide improved systems, apparatus and methodsfor usage, monitoring and management that is simplified and may includebudgeting services. Embodiments may provide improved systems, apparatusand methods for usage, monitoring and management of contracts withutility service providers, and that may enable improved aggregationacross communities and populations of consumers, improved rates,improved rates by aggregation, and improved rate plan optimization.Embodiments may provide improved systems, apparatus and methods forusage, monitoring and management of Distributed Energy Resources (DER).Embodiments may provide improved systems, apparatus and methods forusage, monitoring and management of aggregate smart meter data.Embodiments may make available to consumers aggregate smart meter data,and the same may be provided in a format to facilitate consumptionplanning or real-time decisions to maximize benefit to the consumer.

Embodiments may provide improved systems, apparatus and methodsincluding easily installed consumer electronic devices that may utilize,have capability and provide advanced smart, realtime, or near real-time,power monitoring and control, and which may provide consumers withsmart, load-based consumption information and advice, and that mayprovide for developing advanced usage profiles for such consumers.

Embodiments may provide improved systems, apparatus and methodsincluding creating energy usage models of facilities, such as homes, andfurther may provide for usage of smart devices/appliances or IoT, trendanalysis and self-performed energy audits with respect to such models.

Embodiments may provide improved systems, apparatus and methods forproviding energy savings information and education to consumers.Embodiments may provide improved systems, apparatus and methodsconnecting consumers with 3rd party electric utility service providers.

Embodiments may provide improved systems, apparatus and methodsincluding a central hub for controlling third-party Home Area Network(HAN) devices.

Embodiments may provide improved systems, apparatus and methods forestablishing industry standards for HAN and home automation.

In embodiments, a system may include a smart appliance for use by a userwithin a household. A suitable smart appliance may include a mechanicalcomponent for transforming the state of an object from a first state toa second state by expending a resource; an actuator for initiatingoperation of the mechanical component; a display for displayinginformation to the user; and a wireless communication module foraccomplishing bi-directional data communication over a datacommunication network with at least one of a utility smart meter, autility provider server and an energy management cloud-based server.

In embodiments, a system may include a smart appliance as described inthe preceding paragraph, and that further includes a logic componentincluding a stored series of instructions enabling the smart applianceto receive an input of the actuator from the user, and responsive to theinput, may retrieve historical runtime data of the appliance and acurrent utility rate for the resource used by the smart appliance. Thelogic component may further determine a projected monetary cost forresources to be used by the smart appliance during a current runtimeperiod based on the historical runtime data of the appliance and thecurrent utility rate for the resource. Such an appliance may thenpresent projected monetary cost for the current runtime to the user onthe display.

In various embodiments, the logic component may further enable the smartappliance to determine a projected monetary cost for resources to beused by the smart appliance during a future runtime based on thehistorical runtime data of the appliance and a future utility rate forthe resource and present the projected monetary cost for the futureruntime to the user on the display.

In various embodiments, the user may select a current or future runtimebased on the display of the projected monetary cost for the currentruntime and the projected monetary cost for the future runtime.

In various embodiments, the projected monetary cost during the currentruntime and/or the projected monetary cost during the future runtime maybe further based on, or may reference, at least one of: a currentweather condition, a future weather condition, a resource budget of theuser, a projected occupancy status of the household, and a schedule ofother appliances within the household that will be run.

In various embodiments, the historical data may be retrieved by thewireless communication module from at least one of the smart meters, theutility server and the energy management server.

In various embodiments, the current utility rate may be retrieved by thewireless communication module from at least one of the smart meters, theutility server and the energy management server.

In various embodiments, the historical runtime data may be determinedbased on a load disaggregation algorithm applied to the resource usageby the smart appliance and other appliances within the household.

In various embodiments, the logic component may comprise at least one ofan application-specific integrated circuit (ASIC), an electronicallyerasable and programmable read only memory (EEPROM), or a processor inconjunction with a memory for storing processing instructions to beexecuted by the processor.

In various embodiments, the display may comprise at least one of alight-emitting device (LED) and an electronic ink display device.

In various embodiments, the smart appliance may comprise a refrigerator,a dishwasher, a clothes washer, a clothes dryer, a water heater,heating/ventilation/air-conditioning equipment, a thermostat, an oven, amicrowave oven, and/or lawn watering equipment. In various embodiments,the resource used by the appliance may be electricity, water, naturalgas, heating oil, or any other measurable resource that may be used andmetered by a utility for service to a user operating an appliance.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features believed characteristic of the disclosed subjectmatter will be set forth in any claims that are set forth hereinbelow orfiled later. The disclosed subject matter itself, however, as well as amode of use, further objectives, and advantages thereof, will beunderstood by reference to the following detailed description ofillustrative embodiments when read in conjunction with the accompanyingdrawings. Further aspects of the present disclosure will be appreciatedupon review of the detailed description of various embodiments, providedhereinbelow, when taken in conjunction with the following Figures, ofwhich:

FIG. 1 is a schematic representation of a resource management system 100including a smart appliance and network environment in an exemplaryembodiment of the disclosed subject matter

FIG. 2 is an illustration of an exemplary logic module of a resourcemanagement server 110 shown generally in FIG. 1 .

FIG. 3 is a system diagram showing interconnected components of aresource management server 110 shown generally in FIG. 1 .

FIG. 4 is a schematic block diagram of the operating components of thesmart appliance 102 shown generally in FIG. 1 .

FIG. 5 is a simplified data graph illustration of network communicationsbetween a smart appliance 102 and the resource management server 110shown generally in FIG. 1 .

FIG. 6 is a flowchart of a runtime projection process 600 in anembodiment.

FIG. 7 is an illustration of displays that may be presented to a user ofa smart appliance 102 shown generally in FIG. 1 .

FIG. 8 is an illustration of a screen display of a smart appliance in anexemplary embodiment.

FIG. 9 is an illustration of a screen display of a smart appliance in anexemplary embodiment.

FIG. 10 is an illustration of a screen display of a smart appliance inan exemplary embodiment.

FIG. 11 is an illustration of a screen display of a smart appliance inan exemplary embodiment.

FIG. 12 is an illustration of a screen display of a smart appliance inan exemplary embodiment.

FIG. 13 is an illustration of cyclical data that may be analyzed by aresource management server as shown generally in FIG. 1 .

FIG. 14 is an illustration of cyclical data that may be analyzed by aresource management server as shown generally in FIG. 1 .

FIG. 15 is an illustration of cyclical data that may be analyzed by aresource management server as shown generally in FIG. 1 .

FIG. 16 is an illustration of cyclical data that may be analyzed by aresource management server as shown generally in FIG. 1 .

FIG. 17 is an illustration of cyclical data that may be analyzed by aresource management server as shown generally in FIG. 1 .

FIG. 18 is an illustration of model calculations that may be performedby a resource management server as shown generally in FIG. 1 in anexemplary embodiment.

FIG. 19 is an illustration of model calculations that may be performedby a resource management server as shown generally in FIG. 1 in anexemplary embodiment.

FIG. 20 is an illustration of model calculations that may be performedby a resource management server as shown generally in FIG. 1 in anexemplary embodiment.

FIG. 21 is an illustration of model calculations that may be performedby a resource management server as shown generally in FIG. 1 in anexemplary embodiment.

FIG. 22 is an illustration of an exemplary graph of smart applianceoperation in accordance with outdoor temperature and setpoint asmonitored and generated by the resource management server of FIG. 1 .

FIG. 23 is a simplified schematic illustration of a resource managementsystem 200 in an exemplary embodiment.

FIG. 24 is an illustration of an exemplary logic module 220 of aresource management server 210 shown generally in FIG. 23 .

FIG. 25 is a system diagram showing interconnected components of aresource management system 200 shown generally in FIG. 23 .

FIG. 26 is a schematic block diagram of the operating components of thesmart appliance 202 shown generally in FIG. 23 .

FIG. 27 is a simplified data graph illustration of communications over anetwork between a smart appliance and resource management server, asshown generally in FIG. 23 .

FIG. 28 illustrates an exemplary thermostat employing the presentdisclosure.

FIG. 29 illustrates an exemplary dishwasher employing the presentdisclosure.

FIG. 30 illustrates a further exemplary thermostat employing the presentdisclosure.

FIG. 31 provides a visualization of a time-bounded decision space asemployed by embodiments of the present disclosure.

FIG. 32 depicts an exemplary usage process as employed by embodiments ofthe present disclosure.

FIG. 33 depicts an exemplary snapshot process as employed by embodimentsof the present disclosure.

FIG. 34 depicts an exemplary cost process as employed by embodiments ofthe present disclosure.

FIG. 35 depicts an exemplary decision control process as employed byembodiments of the present disclosure.

FIGS. 36-58 illustrate exemplary outputs via interfaces in exemplaryembodiments of the present disclosure.

FIG. 59 illustrates a method for predictive management of selecting andreceiving retail electric utility service to a residence, in anexemplary embodiment.

FIG. 60 illustrates a system for predictive management of selecting andreceiving retail electric utility service to a residence, in anexemplary embodiment.

FIGS. 61-71 illustrate exemplary retail energy provider contracts andretail rate formulas approximated in graphical form, in an embodiment.

FIGS. 72-75 illustrate exemplary pricing on a wholesale energy market inan embodiment.

FIG. 76 illustrates exemplary price forecasting in a wholesale energymarket in an embodiment.

FIGS. 77-80 illustrate weather data that may be used in embodiments.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

Reference now should be made to the drawings, in which the samereference numbers are used throughout the different figures to designatethe same components.

FIG. 1 illustrates an exemplary data communication network environment100 in which the smart appliance 102 of the present disclosure operates.the resource management cloud-based servers 110 are the hub of thenetwork environment 100, which enables user 106, smart appliances 102,user terminals 104, resource management servers 110, smart meters 116,network interface servers 108 and utility provider servers 114 tointeract as described herein over a data communication network, such asthe Internet. The resource management server (s) 110 may be one or agroup of distributed or centralized network computer servers. Suchservers, like any common personal computer, include well-knownprocessors, electronic memory, network communication hardware, userinterfaces, input/output devices, operating system software, andapplication software suitable for accomplishing the functions describedherein. A suitable computer server may be one or more enterprise networkservers of the type commonly manufactured by CISCO, DELL and IBM. Theresource management server(s) 110 may be configured to perform thefunctionalities described herein through suitable programming in C++,PHP, JAVASCRIPT or the like, and may include a resource managementdatabase 112 for storing the data described herein and databasemanagement software, for example, similar to the Structured QueryLanguage (SQL)-type databases distributed by ORACLE. The resourcemanagement server(s) 110 may be centrally located or may be distributedamong a variety of geographical locations and may cooperate to enable tosocial networking functions described herein. The resource managementserver(s) 110 may act as a “cloud” service to a plurality of users andutility providers.

The network environment 100 includes one or more utility providerservers 114, which are operated by a provider of a resource, such as anelectric company or a water company. The utility provider servers 114may be any type of computer network server of the type described in thepreceding paragraph, that are operative to enable data communicationsover a computer network, such as the Internet. Users will typicallymaintain an account with the utility provider. Such an account will beregistered. In addition to user account information, such as usage andmonetary amounts due for resource usage, the utility provider may alsoprovide utility pricing rates for historical, present and future times.Such data is obtained and used by the system 100 as described herein.

User Terminals 104 may be one or more separate consumer computingdevices that are operative to communicate data bi-directionally with thenetwork server (s) 110 over a computer network, such as the Internet.The user terminals 104 may be owned and operated by separate anddistinct users. Users may be assigned a login to access the resourcemanagement server (s) 110. When interacting with the resource managementserver (s) 110 over the Internet, a display of the user terminal 104 maybe changed and updated to enable the customer to directly interact withthe network server(s) 110 in the manners described herein.

User terminals 104 may be any suitable computing or communication deviceused to accomplish the processes described herein. They may be, forexample, a personal computer, laptop computer, notebook computer, mobiletelephone, smartphone, tablet, personal digital assistant or like deviceof the type commonly manufactured by IBM CORP, DELL CORP. and APPLECORP., and having suitable operating system software (i.e., WINDOWS XP,WINDOWS 8, WINDOWS 10, MAC OS X, SUN OS), application software, visualdisplays, processors, electronic memory, network communication hardware,user interfaces (such as a visible display), and other suitableinput/output devices, as are well-known and suitable for accomplishingthe functions described herein.

The network environment 100 includes one or more smart appliances 102,the features of which will be described herein in more detail below. Thenetwork environment 100 further includes one or more smart meters 116which may communicate wired or wirelessly with the smart appliance 112and/or network interface servers 108.

In certain embodiments, the network environment 100 is implemented overa publicly-accessible computer network, such as the Internet or WorldWide Web, via network interface servers 108, which may be any type ofserver mentioned above. The disclosure is not limited to implementationin any specific network configuration and is readily contemplated toinclude the use of any wireless and/or hard-wired devices operating inconjunction with satellite, microwave, fiber optic, copper, local areanetworks (LANs), wide-area networks (WANs), WIFI, IEEE 802.11-basedprotocols, WIMAX and/or other network configurations. It is readilyapparent that the network environment 100 may be implemented in any typeof system comprising interconnected computers configured to communicatedata with each other using messages transmitted electronically or viaother means, without limitation.

Data may be transmitted between the computers, devices and servers shownin FIG. 1 using any of the variety of data formats including, but notlimited to, Hypertext Transfer Protocol (HTTP), file transfer protocol(FTP), or any other suitable data transmission protocols. Datatransmissions may also be encrypted with any of a variety of knowntechnologies, including secure socket layer (SSL) topologies.

Turning now to FIGS. 2 and 3 , an Embodiment may include a SnapshotModel, described in the following flowcharts, determines the cost of anindividual energy product, which can include present-day options andfuture-day when combined with Present-Day and Futures Market Modules.All these possible combinations form a decision tree that can then beanalyzed mathematically. What the decision tree does is permute throughall the available choices and then recursively follows one choice to allpotential future choices through time. For instance, consider justmonth-to-month contracts projected a year in advance. If, based on auser's geographic location, they had access to an average of 25month-to-month contracts it may set n=25. Embodiments may set out depth,d, to 12. This results in 5,200,300 unique branches in the decisiontree. In practice, branches may be composed of a mix of contracts withvarying term lengths.

Additionally, the effect of the decision tree may vary depending on theconsumer's usage profile. In terms of options the decision tree may bethe same for all consumers, based solely on the market, however thevalue of each branch within the tree may vary from consumer to consumer.The Decision Tree may generate Outcome Profiles (OP), which may then becombined with Consumer Usage Profiles (CUP) to yield Impact Profiles(IP) which are unique to each consumer. The best impact profile wins. Aparticular impact profile may then be directly associated with a branchin the decision tree, which may then determine the short-term decisionthat should be made for the user, among the available options (ChooseNew Contract, Renew Existing Contract, Let Contract Expire, ShutoffService)

In real-time, the system may constantly be reevaluating the environment,tracking the current energy market, predicting future energy market,tracking the consumers usage patterns, weather etc. and putting all thisdata into a system model which behaves as a kind of artificialintelligence. As the data in the model changes, it may be reconstructed,adjusting the decision tree, and regularly generating new impactprofiles for each consumer. If a new impact profile has been detectedwhich has a better value than the current one in place, a decision maybe executed on behalf of the user.

The decision tree is a distributed algorithm. Due to the vast number ofpossible permutations the algorithm is distributed in a custom cloudthat allows for distributed parallel processing. Branches within thetree are broken up and distributed. Machine learning techniques are usedto identify recurring patterns and pre-filter and evaluate branches tooptimize the creation of impact profiles for consumers. Similar patternrecognition is also applied to group similar users with each other.

FIG. 2 is a representation of a consumer module logic component used bythe resource management server of FIG. 1 . The consumer module managesconsumer-related data, which includes data provided by a user as well asdata collected from third party sources on behalf of the user. Theconsumer module receives or monitors and then stores the following datafor each user or residence subscribed to the system 100: userinformation (name, location, contact information, payment information),utility information (name, utility type, utility rates), billing historyfrom various utilities, meter information from smart meters or the like,usage history, registration of home automation devices, thermostatoperation and history, weather data and historical weather information,and building history of the residence or the like occupied by a user.This information may be gathered and stored in various fields of theresource management database 112.

FIG. 3 is a representation of the interconnected components of theresource management system including the resource management server 110of FIG. 1 . As represented therein, an embodiment may take inputs fromutility servers 114, smart meters 116, smart appliances 102 (such as asmart thermostat), and third-party information such as weather fromweather servers available over the Internet. Such data may be stored inthe database 112 for use by the system 100. The resource managementservers 110 use this information to generate Dependent LoadRepresentation, Weather Dependent and Independent Load Separation,Balance Point Determination, HVAC system modeling, other system modelingand analysis, user budget creation, appliance and thermostat managementand presentation of information to a user on a user interface, such as adisplay. Such functions and computations are discussed in more detailwith respect to FIGS. 18-21 below.

FIG. 4 is a schematic block diagram of the operating components of anexemplary smart appliance 102 of FIG. 1 . In general, the smartappliance may be any consumer, household, residential, commercial,business or consumer appliance that utilizes a resource (such as energyor water) to change the state of an object or objects from one state toanother using various mechanical operating components 410. The smartappliance 102 may accordingly include, without limitation, a smartmeter, a refrigerator, a dishwasher, a clothes washer, a clothes dryer,a water heater, heating/ventilation/air-conditioning equipment, athermostat, an oven, a microwave oven, swimming pool pumps andcontrollers, lighting controllers, and lawn watering equipment. Thesmart appliance 102 may be any such commonly used appliances that arefurther enhanced and improved with the following components to enablethe functionalities described herein: a processor 402, a memory 404, adisplay 406, and a communication module 408. It will be understood bythose of ordinary skill that, although specific embodiments describedherein are directed to management of electric utility service to afacility, subject matter disclosed, claimed, and enabled in thisdisclosure, in other specific embodiments may include, instead ofelectricity, any other resource that is supplied by a correspondingutility service provider to a facility. Such other resources mayinclude, for example (without limitation): natural gas utility service;any energy utility service; water utility service; telecommunicationsutility service; broadband data transmission utility service by wire,fiber, or cable connection to the facility; broadband data transmissionutility service by wireless connection to the facility; or premisessecurity service. It will also be understood that, as used herein,“facility” may include a single building, group of buildings, or a unitwithin a subdivided building.

The processor 405 may be any type of integrated circuit processor ormicroprocessor commonly used in computing technologies, such as: anAPPLE AS processor, an INTEL CORE I 7 processor, AMD's PUMA CPU, and thelike. In lieu of a processor used in conjunction with operatinginstructions stored in a memory, the processor 405 may instead beimplemented as an Application Specific Integrated Circuit (ASIC) orother single purpose integrated circuit (IC) device.

The memory 404 may be any type of electronic memory device including,but not limited to: random access memory (RAM) IC chips, read-onlymemory (ROM) IC chips, dynamic random access memory (DRAM) chips, staticrandom access memory (SRAM) chips, first-in first-out (FIFO) memorychips, erasable programmable read-only memory (EPROM), electronicallyerasable programmable read-only memory (EEPROM) chips, a hard disk drive(HDD), a compact disc, a digital video disc (DVD), a memory stick, acloud storage device, and the like.

The display 406 may be any devices suitable for presenting informationvisually to a user. Accordingly, the display 406 may include an LCD orLED device or an electronic ink display of the kind employed by AMAZON'SKINDLE device. The display may also include options for communicatingwith the handicapped by using sound or tactile presentations in place ofvisual displays of data. Other types of display devices may be used withthe smart appliances 102. The size and type of display may vary with thetype of smart appliance on which it is mounted.

The communications module 408 may be any type of data communicationdevice that may be used to transmit computer readable data between thesmart appliance 102 and the other components of the network environment100. In the case where the smart appliance 102 is hard-wired to thenetwork environment 100, the communications module 408 may include acomputer modem, such as a digital subscriber link (DSL) modem a cablemodem, or other type of hard-wired network modem device. In the casewhere the smart appliance 102 may communicate with the networkenvironment 100 wirelessly, the communications module 408 may include awireless fidelity (Wi-Fi) modem, an IEEE 802.11x device, a BLUETOOOTHmodule, a cellular communications module and the like, withoutlimitation. One of ordinary skill in the art will readily appreciatethat the size and type of communication module w408 will depend on thesize and type of smart appliance 102 in which it is embedded.

Turning now to FIG. 5 , an exemplary data graph illustration of thenetwork communications between the smart appliance 102 and the variouscomponents of the network environment 100 is shown. Commencing at step502, a user initiates operation of a smart appliance 100. An initiatingevent may be a scheduled operation, or a command to change a roomtemperature change, and the like. An embodiment may offer a user definedschedule or automate a schedule based on a desired outcome (such as afixed budget). Responsive thereto, the smart appliance 102 requestsruntime information from the resource management server 110 (step 504).In particular, the smart appliance requests information on the amountand/or cost of a resource that it consumes during the type of run cyclethat the user is requesting at a current time and based on currentutility rates. For example, the user may be initiating a wash cycle of adishwasher, that uses electricity and water to clean dishes placedtherein using various mechanical components. Projected Runtime may bebased on an algorithm that takes multiple historical data points (i.e.operating conditions past/present) that contributed to HISTORICALRUNTIME and determines future runtimes.

In one embodiment, the smart appliance may provide the following examplerequest, substantially in the form of a HTTP(S) GET message includingXML-formatted data, as provided below:

 GET /Index.html HTTP/1.1  Host: www.energybill.com  Content-Type:Application/XML  Content-Length: 667  <?XML version = ul.0“ encodinguUTF-8”?>  <auth_request>  <timestamp>2020-12-31 23:59:59</timestamp> <user accounts_details>   <user account credentials>   <user_name>Name@energybill.com</account_name>    <password>abc123</password>   </user account credentials>  </useraccounts_details>  <EnergybillRunCycleProjection>   Request Run CycleProjections  </EnergybillRunCycleProjection> </auth_request>

In response to the request, the resource management servers 110commences an analysis of the projected runtime costs for the smartappliance 102 (step 506) using data on the runtime of the appliance 102that may be stored by one or more of the appliance 102, the resourcemanagement database 112 or the manufacturer of the appliance 102, whichmay be available from a server on the Internet. The projected runtimecosts are also determined using current utility costs for a resourceused by the smart appliance. The current and projected future utilityrates may be provided by utility provider servers 114 and stored by theresource management server 110 in resource management database 112continuously, on a periodic basis, or as needed (i.e., on demand).

Next, the resource management server requests the currentruntime/utility data for the smart appliance from database 112 (step508). In various embodiments, the request may take the form of a SQLdatabase query having the following exemplary form:

 <?PHP  header(‘Content-Type: text/plain’); mysql_connect(“245.39.179.112”,$DBserver,$password); // access database112  mysql_select_db(“APPLIANCERUNTIME.SQL”); // select database  tableto search  $query = “SELECT appliance_id, file_location, file formatFROM ApplianceRunTimeTemplate WHERE run cycle_type LIKE ‘%’ $normalwash”;  $result = mysql_query($query); // perform the search query mysql_close(“APPLIANCERUNTIME.SQL”); // close database access   ?>

Next, in response to the request above, the appliance runtime data isretrieved from database 112 (step 510). Likewise, the current and futureutility rate data for the resource used by the appliance is retrieved(step 512). Finally, weather data and other external data, such asoccupancy, runtime of other appliances, user's established budget andthe like is retrieved from the database 112 (step 514).

Next, the retrieved appliance runtime and utility cost data is providedto the resource management server 110 (step 516). Responsive thereto,the resource management server 110 calculates current and future runtimecosts for the run cycle selected by the user (step 518). For example, aCurrent Cost Algorithm determines computations of projected cost for acurrent run time using load disaggregation and current utility rates. AnOptimal Cost Algorithm determines computations of projected lower costsfor future runtime based, for example, on lower rates, weather, a user'senergy budget, projected occupancy status, and schedule of otherappliances that may be run. Other manners for making thesedeterminations with respect to certain smart appliances are depicted anddescribed in more detail with respect to FIGS. 18-21 below.

Next, the resource management server 110 sends the calculatedprojections to the smart appliance 102 via the network environment 100(step 520). Responsive to the reception of the information, the smartappliance displays the current and future runtime cost data to the user106 on a display 406 of the smart appliance 102 (step 522) The user 106may then choose to run the smart appliance 102 at a current or futuretime based on the presented costs.

It will be readily appreciated that suitably programmed smart appliances102 may perform all the runtime calculations and computations describedabove locally instead of relying on the resource management server 110to do so.

Turning now to FIG. 6 , therein is depicted a flowchart of a runtimeprojection process 600 performed by the smart appliance 102 of FIG. 1 .First, a user requests initiation of a run cycle of an appliance (step602). Next, the appliance 102 retrieves historical runtime data of theselected operating cycle (step 604). Such retrieval may be from a localdatabase or remotely from the database 112 via the network environment100. Next, the appliance 102 retrieves current and future resourcepricing data from the utility that provides the resource used by theappliance 102 (step 606). Such retrieval may be from a local database orremotely from the utility provider server 114 or the database 112 viathe network environment 100.

Next, the appliance 102 determines and displays the projected costs fora current runtime (step 608). Such projections may be calculatedaccording to FIGS. 18-21 either locally, where the smart appliance 102is suitably programmed or by calculations performed remotely by theresource management server 110.

Next, the appliance 102 retrieves weather, user budget, projectedoccupancy and runtimes of other appliances of the user (step 610). Theappliance may need to choose the desired future run time to meet someother user settings, such as running the oven before dinner or the waterheater before bed, etc. The appliance then computes projected costs forfuture runtimes of the requested run cycle based on these externalfactors (step 612) in the manner depicted in FIGS. 18-21 .

Next, the appliance 102 determines whether any projected future runtimecosts are lower than the projected cost for a current runtime (step 614)If so, the process 600 continues to step 616 below. Otherwise, theprocess 600 continues from step 614 to step 620, described later below.

At step 616, the appliance 102 displays the lower cost for a futureruntime to the user 106 on a display 406 (see, e.g., FIG. 7 ). The user106 may then select a current or future runtime (step 618). Next, theappliance 102 runs in accordance with user selection of a run cycle(step 620).

Finally, the actual runtime data and resource usage of the appliance ismonitored during the run cycle in order to update models of applianceresource usage for making better future projections (step 622). Thisinstance of the process 600 then terminates.

FIG. 7 is an illustration of the displays of information that arepresented to a user of the smart appliance 102, as described herein.

FIGS. 8-12 are illustrations of further screen displays that may bepresented to a user by the smart appliance 102 or a user terminal 104.In FIG. 8 , therein is depicted a screen display of a resource usageprojections determined for a user, which may be accessed via userterminal 104. As shown therein, the user may be presented with currentresource usage, historical resource usage and a projection of totalmonthly costs for the resource based on such usage, as determined by theresource management server 110.

FIG. 9 shows an exemplary appliance interface screen display that may bepresented to a user on user terminal 104, where the smart appliance 102is a smart thermostat. Accordingly, the user may be presented with thecurrent temperature setting, the current temperature readings andhistorical or projected future temperatures. Options for changingthermostat setpoints may also be presented.

FIG. 10 shows an exemplary resource budget screen that may be presentedto a user 106 on the user terminal 104. The display may include amonthly budget established by the user, and a current cost of theresource actually incurred for the month. The user 106 may thus monitorresource usage over the course of a month (or other selected timeperiod) in a manner that is readily intuitive to most users.

FIG. 11 shows an exemplary resource usage interface screen display thatmay be presented to a user on user terminal 104 by system over thenetwork environment 100. This is a proof of a user interface screen forconsumer usage information. On this screen the user is able to see thetrend of all their historical and current usage, with weather dependentand independent loads separated. Comparisons may then be made betweenwindow in time and another. With the current billing informationconsumption data is presented to the user in terms of cost. Based on allthe historical data, projections are made for future usage patterns,shown in orange in the proof. Current data being measured in real-timefrom each data source is then used to predict what future usage patternsmay be, and what the cost of those predicted patterns are. Based onhistorical trends, and feedback from the user, a budget is automaticallycreated for the user 106, helping them to monitor their daily, weekly,and monthly consumption and make effective and practice pattern changesto work towards saving energy and money.

FIG. 12 shows an exemplary data trend screen display that may bepresented to a user on user terminal 104 by an embodiment over thenetwork environment 100.

FIGS. 13-17 are illustrations of cyclical data analyzed by the resourcemanagement server of FIG. 1 to determine operation of the smartappliance. FIG. 13 shows the cyclical variation of annual outdoortemperatures measured for a geographic location of the user 106. FIG. 14shows the cyclical variation of daily temperature measured for ageographic location of the user 106. FIG. 15 shows a combination of thedata from FIGS. 13 and 14 , in which an annual profile of oscillatingdaily temperatures is measured throughout the year. FIG. 16 shows howthe temperature variations in FIGS. 13-15 affect a user's load profileon a monthly basis with respect to resource usage. For example, the HVACsystem usage may vary over the course of a year in accordance with dailyand annual temperature oscillations.

FIG. 17 shows interval data of resource usage as may be monitored bysmart meters 116. With resolution of, say, fifteen minutes, anembodiment may be able to identify weather dependent and independentloads. The weather dependent load, in turn, is a good estimate ofinternal heat gain (IHG) as used in later calculations described below.

Continuous monitoring of such data allows models to be built of resourceusage based on weather. Load disaggregation for identifying thecontributions of individual appliances 102, as well as determination ofother weather-dependent and independent variables are performedaccording to the calculations depicted in FIGS. 18-21 .

Turning now to FIGS. 18-21 , therein are illustrations of modelcalculations used by the resource management server 110 of FIG. 1 , inorder to, for example, to determine runtime operation and costs of asmart appliance 102. FIG. 18 shows a formula and data variables used tocalculate the Weather-dependent load disaggregation of a user'shousehold. FIG. 19 shows the formula and variables used to make aBalance Point Determination for the user's residence. FIG. 20 shows amethod for determining Balance Point Depression (BPD) for a user'sresidence based on collected data. FIG. 21 shows a formula forprojecting the runtime of an appliance based on the constants determinedfrom the calculations of FIGS. 18-20 .

FIG. 22 is an illustration of an exemplary graph of smart applianceoperation in accordance with outdoor temperature and setpoint asmonitored and generated by the resource management server of FIG. 1 .This information may be presented to a user 106 system 100 on a displayof the user terminal 104. As shown therein, setpoint temperature andcurrent temperature may be displayed against the on/off operatingruntime cycle of an appliance, in this case HVAC components. Similarinformation may likewise be presented for other appliances 102, such aselectrical usage in kilowatt-hours (kWh) of a clothes dryer over time.Similar data and modeling maybe performed for each resource-utilizingappliance having weather-dependent and/or weather-independent variables.

Referring to FIG. 31 , in an embodiment, a method 660 may includedecision modeling such as by providing a mathematical decision model,such as a decision graph as shown, comprised of nodes and arcs that canthen be analyzed mathematically. The decision graph shown in FIG. 31 isan exemplary visual representation of a mathematical decision model on apartial, very small scale. This graph represents time bounded decisionsover a space that is 12 time unit lengths deep. In our case time unitsare in months and start from the present day at node 1 and look forwarda year into the future. Arcs represent the fixed term length of a retailenergy contract, representing a single 1 month, 2 month, 3 month, 6month, 9 month, and 12 month contract between nodes in this graph. Thesetime bounded decisions then form into various paths. For example aconsumer could follow a path that looks like 1119 or 131313 or 66, or12, choosing different contracts throughout the 1 year period. For thisparticular graph, there are 1059 different paths that can be chosen froma relatively small number of total market choices. In the real worldwhere a consumer can have many hundreds of present day choices this canresult in a total decision space that is very large. For instance,consider just month-to-month contracts projected a year in advance. If,based on a user's geographic location, they had access to an average of25 month-to-month contracts we could set n=25. We would set our depth,d, to 12. This results in 5,200,300 unique paths in the decision graph.But, in practice the numbers are many orders of magnitude larger thanthis. There are actually many thousands of choices, arcs will becomposed of a mix of contracts with varying term lengths, not just monthto month, and many markets offer term lengths as long as 36 or 60 monthswhich can result in very deep models to consider their value.

Shown in FIG. 32 is a method 700 for forecasting or predicting theenergy usage for a consumer. In embodiments exemplified by method 700,such forecasting or predicting may be based on time, and/or weather,and/or appliance schedule. It will be understood that such forecastingor predicting may take into account usage history, weather history,appliance history, building characteristics, and other factors impactingpredictive value.

Referring to FIG. 33 , it will be understood that in an embodiment,method 800 may include a model and output prediction of an energy marketsuch as a retail energy market. It will be understood that a model maywork in part by disaggregating individual energy products within themarket and utilizing machine learning techniques to forecasting similarbut fictitious versions of those products at some time in the future,such as at monthly intervals, based on influencing conditions such asweather, the economy, and other market factors that can have aninfluence on energy price. The model may provide a probabilitydistribution for different types of energy products. Type variationsinclude things such as retail energy contracts of varying term lengthsand distinct rate formulas which taken on different shapes such as flatfixed rate, linear vs nonlinear, time varying vs. usage varying, etc.,as well as other differences that can and do exist between energyproducts in in a competitive retail marketplace. For example, the modelmay predict the likelihood of seeing a retail electric plan with a 3month term at $0.05/kWh nine months from now, or a similar plan with a 6month term at $0.045 and a base charge of $12.99/month. The output ofthe model may provide a distribution of all such potential energyproduct variations in the future, yielding as a combined space of bothpresent-day and future-day predicted retail choices.

Illustrated in FIG. 34 is a method 900 for determining the cost of anenergy product (for example, from method 800) based on a usagemeasurement or prediction (for example, method 700). It will beunderstood that, in an exemplary embodiment, the cost of an energyproduct may be provided from method 800 as shown in FIG. 33 . It will beunderstood that, in an exemplary embodiment, the usage measurement orprediction may be provided by method 700 as shown in FIG. 32 .

Referring to FIG. 35 , in an embodiment, a method 1000 may includeanalyzing a decision model, such as a decision graph, to identify a bestdecision path and at least one best initial or short-term decisionconsistent with the identified best decision path. As shown in FIG. 35 ,it will be understood that analyzing may be achieved by processing adecision model with a suitable combinatorial optimization decisionprocessing algorithm such as, for example, a single shortest path (SSP)or k-shortest path algorithm (KSP). Due to the large decision space,such decision processing algorithms may be engineered to run bothparallel in high performance computing (HPC) environments such as CUDAby Nvidia, as well as distributed in a cloud of interconnected servers.Additionally, the effect of the decision graph will vary depending onthe consumer's usage profile. In terms of options the decision graphwill be the same for all consumers, based solely on the market, howeverthe value or cost of each arc within the graph will vary from consumerto consumer. It will be understood that a system may include a decisionmodule that may generate a final graph that is weighted unique to eachconsumer. This final graph may then be searched for the top K paths inthe graph, which are further processed to yield a final prediction of asingle best path. This is a best path that takes into account the usageprofile of the user, utility rate structure, monthly bill and budget,smart appliance schedule and setting preferences, as well as otherinfluencing factors like weather and a dynamic energy market. Ultimatelythe decision may include a comparison between a present-day andfuture-day state, for which can then be determined the short-termdecision that should be made for the user, among the available options.Choices may include, for example, an initial or short-term decision ofentering a new (different) contract, renewing an existing contract,allowing a contract to expire, or shutting off service. Referring toFIG. 35 , method 1000 may include repeatedly updating and re-determiningor reconstructing a decision model. It will be understood that suchreconstructing may be continuous or substantially continuous. Referringto FIG. 35 , in embodiments method 1000 may include reevaluating theenvironment, tracking the current energy market, predicting futureenergy market, tracking the consumers usage patterns, smart appliancesettings and schedule, weather etc. and putting all this data into asystem model which behaves as a kind of artificial intelligence. As thedata in the model changes, it will be reconstructed, adjusting thedecision graph, and find a new best path to compare the present state.If a best path has been detected which has a better value or cost thanthe current one in place, a decision will be executed on behalf of theuser.

Referring to FIG. 27 , in embodiments, a method may include controllinga smart appliance according to a budget. It will be understood that suchcontrolling may be similar to selecting a contract as elsewheredescribed herein. A decision model or decision graph may be generated,based on relevant criteria. The time scale for controlling a smartappliance may be in minutes or seconds, and a time bounded decision mayinclude, for example, running a smart appliance for some operation cycleor a combination of operation cycles. Like a deregulated contract, thisarc in the network will have a cost associated with it. By traversingthe potential control choices for the appliance (like a thermostat), inembodiments budgetary goals and environmental preferences andconstraints may be maximized or facilitated by following a particularcontrol path for the smart appliance. The decision/control moduleincludes aspects of both decision identification for contract selectionand control of smart appliances.

It will be understood that, in embodiments, a smart appliance may runirrespective of the user monitoring events and may self-report.Third-party events, such as weather change, may cause changes, andanalytics may be performed. Analytics modules may include, for exampleand in part, Usage Module, Cost Module, and Decision/Control Module. Itwill be understood that when analytics are performed, each time they areupdated, a simple model is generated and is provided to the smartappliance. In an embodiment, final cost may be reported by model runninglocally on a smart appliance. This may prevent the smart appliance fromhaving to constantly go back and forth to the server. Data exchanges mayoccur only when necessary, or according to schedule, and may allow costto be reported instantaneously to the user without the delay of waitingon analytics to calculate. In an embodiment, accordingly, cost may beprecalculated. So locally on the smart appliance a relatively simplemodel, like a simple formula, may be processed with basic variables suchas device settings (wash cycle or whatever) to provide cost. In anembodiment, cost might be reported in various ways such as, cost for asingle cycle or aggregate cost for a day for maintaining a particulartemperature setpoint. Depending on the type of smart appliance, cost maybe reported in a variety of ways for meaningfulness.

In an embodiment, a runtime model may include a formula, an automatedrate sheet or menu updated menu wherein prices change. A smart appliancemay utilize or may be controlled according to such a runtime model,which is executed locally on the device/appliance, to present a cost tothe user. Such a model may be produced from all the complex analyticsthat may result in a formula which may be performed on the smartappliance. From time to time, as events happen (weather, market, otherappliance in the home, new usage data from the smart meter, etc., andwhatever), the backend system will detect some new information thatwarrants we rerun our various analytics models, Usage, Cost, and so on.After this happens, a new runtime model would be generated, and thenthat would get pushed down to the appliance if/when data connectivity isavailable. IoT devices often have spotty connections, and they likelysit behind routers and firewalls. So rather than the server pushing downto the appliance, in an embodiment, a smart appliance may periodicallycheck for a new runtime model. In an embodiment, a smart appliance maypush update up to the server for runtime history. But, this may beperformed separately, or alone, or on periodically such as hourly.

In an embodiment, a smart appliance may interact with other 3rd partydevices while offline. For example, in an embodiment, a smart appliancemay interact with other smart appliances or devices for improvingreporting accuracy, such as while offline or between updating of runtimemodels from the server. This may occur, for example, where highlyunpredictable consumer usage or weather patterns are presented orencountered, or where internet communications are down so as to preventupdating of the runtime model and system for a long period. and thesystem couldn't update.

In addition to the foregoing solutions, an embodiment may also offer thefollowing functionalities:

An embodiment in contrast to providing a cost for running an appliance,may provide an incentive for not using the appliance, not using energy,or supplying energy back to the grid by the appliance. An embodiment mayemploy a combination of cost and incentives. Incentives may include forexample: cash rebates.

An embodiment may facilitate a social network for energy consumers,allowing energy consumers to compare themselves with others in theirarea and connect online. Users may be able to share their energyconsumption stats, as well as savings stats when changes in usagepatterns are detected to create a “crowd-saving” effect. Users may thenconnect with each other to share and discuss what they did to saveenergy and reduce their bills, offer or ask for advice, etc. Anembodiment may also embed into and interface with existing socialnetworking sites like FACEBOOK and GOOGLE+.

An embodiment may be a cloud-based software system for performingwhole-building residential and commercial energy audits using mobiledevices. This software is based on an existing desktop softwaretechnology that was widely used by and sold to utility companies andregarded as superior to and a replacement for the Department of Energy'sDOE-2 simulations, as used in the past. The improved system offered mayallow commercial or residential consumers to manually or automaticallyself-perform energy audits of their home with ease using their userterminals and mobile devices.

An embodiment may serve as a kind of ANGIE'S LIST for energy-relatedservice providers with some additional caveats that are patent-pending.An embodiment may allow providers to be listed by area and reviewed byusers. If energy saving services are provided, such as HVAC replacement,additional insulation, installation of DER or appliance upgrades, anembodiment may track the before/after energy consumption for this userbased on the date of service and use this data to additionally rate theservice for its measured effectiveness. An internal rating system may beapplied based on the effect of the service to the consumer's energybill. Highly effective services may be given priority ranking in thesystem and recommended to other users.

In conjunction with all other services provided, various informationalareas may be maintained to aggregate and offer energy education to thepublic ranging from general to advanced-level. Educational programs mayalso be offered to increase public awareness and promote the services tothe general public and through public schools.

Energy product choices vary by geographic location and time. At anygiven point in time, day-to-day, month-to-month, the number of optionsmay change, and this is usually counted in the dozens to hundreds for aconsumer. As of now, just in the Texas electrical power market there areover 1,500 different product choices. A consumer who resides in zip code77486 has access to about 200 of those.

The best, lowest-cost, choice is not a simple matter but requirestime-varying analysis which takes into account the consumer's usagepatterns, market predictions, and the impact of present-day choices onfuture-day choice options. This can be thought of as a kind of“butterfly effect,” where the small variance in initial conditions mayhave a dramatic effect on the outcome further down the timeline. Inorder to make the best choice today, the long-term impact of each choicemust be considered. What seems like a good choice today may be a verybad choice in the long-run.

For instance, a user today may have the option of signing up for amonth-to-month, 3 month, 6 month, 12 month, 24 month, or 36 month energycontract. And the rate ($/kWh) for each varies by usage patternsmonth-to-month (as described previous). Based on usage patterns alone itmay be more cost effective for a consumer to alternate between variousshort-term contracts. This is complicated even further however by theimpact of present-day choice on future choice options.

If a user were to sign a long-term contract in the summer this is verylikely a bad choice. While the contract being chosen may seem like thebest rate today, the rate is likely very high because it's duringpeak-load market conditions. Rather than choosing the long-term contractnow, it may be more cost-effective long-term to choose a shorter-termcontract now, which may have a higher rate, and then plan on choosing alonger-term plan next month or a few months later after electrical loadon the grid eases and market prices drop. Long-term choices may alsobring early termination fees and other considerations which impactfuture options.

Determining the best choice requires modeling a series of choices backto back. For instance, if the consumer goes month-to-month, what maytheir product choices be one month from now when that one-month termexpires? Depending on the depth of projection (e.g., one month vs.thirty-six months in the future) models may use a mix of actual versuspredicted data.

For reasons of cost and in-house technical ability, utilities companiesand city and state governments have been unable to provide advancedtechnology services to energy consumers. Minimum services are providedfor things such as account management, energy management or budgeting,Distributed Energy Resource (DER) management and utility and billpayment, which are sometimes unreliable and usually lacking in advancedfeatures that would be useful to consumers for energy management.Utility companies in regulated markets know that customers have nochoice in their provider, so they have little incentive to improve thearray of services customers have access to. Even in deregulated markets,it is not necessarily in the monetary interest of the utility companiesto help customers save energy, resources and money. There is littledirect-cost benefit for the utility companies to invest in energysavings programs other than what is required by governing entities.Embodiments of the disclosure provide unique hardware and softwareproducts that may fill this gap in the market and empower consumers tocapitalize on real-time or future energy consumption decisions oropportunities.

Deregulated utility market consumers are left to their own devices todetermine which energy contract to choose among a pool, which mayinclude potentially hundreds of different contracts offered by a growingmultitude of different providers, varying by the particular market. Thedecision of which contract to choose, whether they should renew orextend, or switch contracts or providers, and when, is very difficult.The variable factors that go into optimizing their rate planopportunities is comparable with trading stocks on the stock market,requiring an advanced understanding of the energy market and the energyconsumption characteristics of the residence.

Embodiments of the present disclosure provide a solution including anautomated professional evaluation of available energy rate plans andrenewal of deregulated energy products to save users money bymaintaining energy bills below the average for their respective markets.Embodiments may automatically trend and evaluates the energy market,matches energy consumers with the best provider and available rate planbased on the characteristics of their residence, their personal usageprofile and market history and projections, and then sign consumers up,execute contracts, and facilitate account management. The savings may bemeasured by monitoring the energy usage characteristics of the user'sresidence and retail price paid per energy unit versus the averageretail price paid for that energy product in that market (a trailingtwelve month, TIM). The TIM data and retail/wholesale market projectionsis compiled from various national data sources (e.g., Energy InformationAdministration (EIA), Federal Energy Regulatory Commission (FERC),Department of Energy (DOE), etc.) and real-time energy pricing fromregional energy authorities (i.e., Texas Electric, ERCOT, and the like).

An embodiment may utilize historical meter data a model or profile of aconsumer's usage pattern of the resource. An embodiment may utilize thisdata along with TIM data and other predictive indicators to determinethe most informed decision on behalf of users for rate planoptimization, so as to maintain below-average energy bills when comparedto average/mean/median users in their market.

As another problem in the current marketplace, customers are burdenedwith having to learn how to use a new web site every time they changeenergy providers, which may be quite frequently, often several times peryear in deregulated markets. They may also have to maintain multipleaccounts at once, such as for water, electricity, and gas utilities. Anembodiment may provide one account to manage all utility bills. Usersmay have a single sign in (SSO) account that they may log into in orderto manage electric, natural gas, water contracts, bills, payments andhistory. Users may access and manage all of their accounts through onecentral service. Historical account data may be preserved even asproviders change, archiving all data indefinitely and making itaccessible to appropriate users. While this service is primary targetedfor deregulated markets where service changes are more frequent, it mayalso be available to regulated and mixed markets. Multiple users mayalso be able to access a shared account, such a multiple people in asingle household or residence. Shared account access may empower otherstakeholders in a shared accountability and/or energy monitoring role.

Another problem in some deregulated energy markets occurs when usersswitch providers, wherein access to all historical usage information istypically lost to the user after the change. This also occurs inregulated markets when customers move and change residences. Also, inboth deregulated and regulated markets, the level of data users areprovided access varies by the specific utility, but is typically lackingin comparison to the level of historical analysis. An embodiment mayinclude long-term archiving of historical billing (monthly costs), usersactual retail price per energy unit (by month and year), historicalaverage retail prices paid in the market and monthly savings (comparisonof price paid versus average retail market price). This service may alsobe available to regulated customers whose providers may not offer such aservice, and who also may want to maintain historical data betweenresidences after moving or want to benchmark long term energy efficiencychanges at their residence.

There is currently no way to aggregate usage data across multipleproviders in deregulated markets. As users change providers, access tohistorical data is lost, and data cannot be trended across multipleproviders. Also, even in regulated markets, historical data is oftenarchived for only limited periods of time, and a user's ability toaccess that data is highly limited and usually not particularly usefulto the customer. An embodiment may gather historical monthly energyusage from past and present monthly bills, which may be archived andcontinuously compared for both deregulated and regulated users. Thisinformation can be used to identify usage trends and create monthly andannual usage profiles, which may aid in deregulated contract decisions,usage and cost projects, and also energy audits for savings analysis.Advanced tools may be provided which allow consumers to view and analyzetheir usage trends, viewing data in graph and spreadsheet form, andperform relevant math functions such as estimate future bills based onhistorical trends.

According to the US Energy Information Administration (EIA), in 2012,five hundred and thirty-three electric utilities had over forty-threemillion advanced “smart” metering infrastructure (AMI) installations.Smart meters allow usage data to be accessed remotely and upon requestby the utility owning them. As another problem in the currentmarketplace, though, many customers have smart meters that are installedat a residence to monitor usage of a resource, but most users either donot have access to smart meter data or don't know how to access it. Theinformation gap to gain access to it is too large for most people, orthe data isn't available in a format that is friendly or useful to theconsumer. Even in those rare instances where smart meter data isaccessible by users, its usefulness is limited to the experience of theperson accessing it. Average consumers do not typically understand themeanings of kilowatt-hours and are thus unable to understand and makeuse of the smart meter data.

An embodiment may aggregate data at regular intervals and log it withthe user's account. Smart meter data is retrieved at the meter'savailable capacity, such as fifteen-minute intervals, thereby allowingquarter-hour, hourly, daily, weekly, and monthly profiles to be created.Such detailed usage profiles may aid in creating models to be used inderegulated contract decisions and facilitate off-peak billing options.These models also accommodate more advanced energy audits and personalenergy analysis and education, allowing for analysis based on hourly anddaily usage rather than just monthly. The data may instantly be pulledinto web and mobile tools that allow customers to analyze it, using itto better understand their bill, energy usage characteristics, and howto save resources and money on their utility bill. An embodiment mayemploy tools to allow users to study the impact of specific loads intheir home, such as contributed by an HVAC component or other appliance.

An embodiment may allow users to track their energy usage in real-timeand project their end-of-month bill based on usage patterns. Users maybe able to set budget limits and then be guided to tailor theirconsumption so that they don't exceed that budget. Smart devices, IoTand Distributed Energy Resources (DER) can also be interfaced with andautomatically controlled to manage the budget for all utility bills andto maximize the overall benefit of the user.

Another problem in the current marketplace is that an increasingly largenumber of homes are equipped with “smart” thermostats and other homeautomation devices, but these devices are not typically well utilizedand tend to just be expensive gadgets with excessive schedulingcapabilities, offering capabilities that are ultimately unused. Smartthermostats, other smart devices (IoT), and Distributed Energy Resources(DER) need to be utilized in the context of overall energy billmanagement.

Responsive to this issue, an embodiment's software may remotely monitorand control smart thermostats such as NEST, ECOBEE, and HONEYWELL WIFIST, as well as other home automation devices currently planned for themarket. Comparable with smart meters, smart thermostat data may bepolled at regular intervals and logged, allowing for historical trends.In areas where smart meter data is available this may be an especiallypowerful combination, allowing HVAC operational parameters and usepatterns to be correlated with overall energy consumption. Consumers maythen be able to set specific parameters limiting their total energyconsumption, and an embodiment may be empowered to regulate HVAC use inorder to maintain a specific energy bill. A consumer may for instanceset a budget of $200 for their monthly bill, and an embodiment may thenregulate the temperature in the home and schedule other appliance usageas necessary to not exceed this bill.

Another common problem in the current marketplace is that there is notcurrently any industry-wide standardization for or building automationsystems. In order to provide automation and control capabilities, deviceand appliance manufacturers are forced to venture into territory thatthey frankly have no expertise in. Companies with expertise in designingand manufacturing home appliances, like air conditioners and clotheswashers, are typically not experts in software and the web, which isnecessary for home automation networks. Because of this most applianceswith home automation end up offering limited functionality in comparisonto its potential. For home automation and control to be truly useful, itneeds to be combined with resource/energy bill information, such asconsumption data from smart meters, billing information from utilities,individual usage profiles, generation rates, transmission rates, weatherforecasts, market forecasts, etc. A home automation device by itself isreally just a gizmo, and only becomes powerful when it is employedwithin the context of total resource management.

Additionally, device makers are currently all acting independent of oneanother, and this creates a burden which is simply unfeasible. Makersend up reinventing the wheel with each device that they engineer,resulting in lower quality products and substantially increasing theirown engineering costs, and ultimately, the costs to their consumers.From the consumer's perspective things get even worse. Each competitivedevice may have its own unique software system for controlling it. So,users then have to keep track of differing software functionality andoperation. Currently there is no way to centralize all home automationand resource management into one central software system.

To address these issues, an embodiment of the present disclosure maysupport all existing and future Home Area Network (HAN) or other HomeAutomation devices. Through use of the present disclosure, users may beable to register devices and control them through the system.Embodiments may serve as a “virtual” operating system that allows usersto easily control all the smart devices in their home through onecentralized system. These devices can then be used in the context oftheir whole energy bill and as a Distributed Energy Resource (DER).

An embodiment may also provide a marketplace to help promote third-partyhome automation and control devices. An embodiment may let users reviewdevices and, in conjunction with their metering data, may measure theeffectiveness of those devices and be able to rank them internally.

Presently, there is not currently any technology that allows consumersto separate loads and correlate this with billing data, allowingconsumers to understand their usage patterns and ultimately theassociated cost of use. Consumers are not empowered with the ability tounderstand the cost of energy use before they use it and only see theresults of their energy consumption on the energy bill. The inability tomonitor individual loads and associate a value makes it difficult forenergy consumers to intelligently save money or effectively conserveenergy for a maximum benefit for both the user and the energy market.Companies who are currently working on load disaggregation and otherrealtime metering devices lack standardization, access to utility andbilling data, and the ability to perform energy analysis on metereddata. There is a lot that can be done with metering data beyond simplyidentifying loads. By itself load disaggregation is not particularlyuseful to consumers, and the technology is inherently limited in itsaccuracy.

Load disaggregation is where electrical loads are disaggregated, orseparated, individually on a particular circuit from a singlemeasurement point. For example, a smart electric meter can monitor thepower consumption for an entire house. An electrical load is anythingthat draws power, such as an air conditioner or dish washer, television,or cell phone charger. Through a variety of mathematical methods,discussed below, and depending on the resolution of the metering data,individual loads of various separate appliances within a residence canbe readily identified. Similar methods of load disaggregation can beused for other types of resources such as gas usage and water usage.

By separating loads utilizing load disaggregation algorithms, anembodiment may then monitor and profile the power consumed by eachappliance. For instance, an embodiment may tell the consumer whatpercentage of their overall energy bill is contributed by the HVACversus the clothes washer. Consumers would be able to see exactly howmuch their clothes washer costs to run, to the penny, and not just overan extended period of time, but down to the cost of an individual cycleof clothes. Consumers may also see how much it costs every time theyopen the refrigerator door. This may empower consumers to make energyuse decisions in advance, for example, whether it's worth it to wait towash the dishes until the whole dishwasher is full rather than doing ahalf load, or whether it's worth it to set the temperature at 72 degreesin the middle of the summer instead of 74. The impact of such decisionsmay be presented to consumers in real time. Customers may be able tomake informed decisions and choose what they are buying or selling inadvance. Utility bills may be presented in receipt-style like a grocerybill, separating each electric load and the associated cost.Disaggregation of energy data may also allow for very advanced systemsmodeling, which would also be useful for troubleshooting or newappliance recommendations. For example, determining whether an HVAC isoperating at decreased deficiency and should be serviced or replacedaltogether, or whether there is a toilet continuously running water andneeds to be repaired. Real-time energy data can be combined with ourcalculation engine for automated energy analysis. Based on theconsumer's home usage profile, embodiments may automatically simulatetheir entire home and project how much they would save if they switchedto a different model appliance and what the real projected paybackperiod would be using real utility rates and energy use characteristics.

An embodiment may utilize Circuit Signal Acquisition Meter (SAM), whichallows for load disaggregation. Circuit SAM was developed as anelectronic device that energy consumers may self-install on their homesor commercial facilities, allowing them to identify and separateelectronic loads. Circuit SAM is a non-intrusive, load-monitoring (NILM)device that is operated by using multi-dimensional signal processing toanalyze the energy consumed by a home or facility. Loads are separatedthrough harmonics on the power curve. Loads generate electricalsignatures that can be identified through pattern recognition, i.e.,power consumption fingerprints.

FIG. 59 illustrates a method 1100 for predictive management of selectingand receiving electric utility service of a facility, such as aresidence, in an exemplary embodiment. In an embodiment, method 1100 maybe performed by a processor accessing a non-transitory computer readablemedium including executable instructions which, when executed in aprocessing system, causes the processing system to perform the steps ofmethod 1100 as disclosed herein. Method 1100 may include performing 1101a utility service modeling engine. Such performing 1101 may include,such as in real-time, continuously re-evaluating the environment,tracking the current energy market, predicting the future energy market,tracking the consumer's usage patterns, weather etc., and updating allsuch data in the system modeling engine. The utility service modelingengine thus may behave or serves as an artificial intelligence engine.As data in the utility service modeling engine is updated and changes,the possible predicted paths may be reconstructed, and the decision treemay be adjusted where superior predicted paths come into existence. Forthe predicted rate plans, new impact profiles for each consumer may begenerated. If a new impact profile has been detected, which provides abetter value than the current one in place for the customer, a decisionmay be executed on behalf of the user.

In an embodiment, the decision tree may be a distributed algorithm. Dueto the vast number of possible permutations the decision tree algorithmmay be distributed in a cloud, such as a custom cloud that allows fordistributed parallel processing. Branches within the decision treealgorithm may be broken up and distributed. Machine learning techniquesmay be used to identify recurring patterns and to pre-filter andevaluate branches to optimize the creation of impact profiles forconsumers. Similar pattern recognition also may be applied for groupingsimilar users with each other.

Method 1100 may include managing 1102 consumer-related data of aconsumer module logic component. As previously discussed above inrelation to FIG. 2 , the resource management server of FIG. 1 mayinclude a consumer module logic component. In managing 1102, theconsumer module logic component may manage consumer-related data, whichmay include data provided by a user as well as data collected from thirdparty sources on behalf of the user. The consumer module logic componentmay receive or monitor, and store, the following consumer-related datafor each user, facility or residence: user information (name, location,contact information, payment information), utility service providerinformation (name, utility type, utility rates), billing history fromvarious utility service providers, meter information from smart metersor the like, usage history, registration of home automation devices,thermostat operation and history, weather data and historical weatherinformation, and building history of the facility, residence or the likeoccupied by a user. Managing 1102 may include gathering and storing thisinformation in various fields of the resource management database (suchas database 112 in FIG. 2 ).

Method 1100 may include variable modeling 1103. As previously discussedabove in relation to FIG. 3 , variable modeling 1103 may includemodeling interconnected components of the resource management system ofa resource management server as shown in FIG. 1 . In an embodiment,variable modeling 1103 may include taking inputs from utility servers,smart meters, smart appliances (such as a smart thermostat), andthird-party information such as weather from weather servers availableover the Internet. Variable modeling 1103 may include storing such datain a database for use by the system. Variable modeling 1103 may includeusing this information, such as by resource management servers, togenerate Dependent Load Representation, Weather Dependent andIndependent Load Separation, Balance Point Determination, HVAC systemmodeling, other system modeling and analysis, user budget creation,appliance and thermostat management, and for presentation of informationto a user on a user interface, such as a display. Performing 1101,managing 1102 and variable modeling 1103 may include performingfunctions and computations as discussed in detail with respect to FIGS.18-21 above.

As shown in FIG. 59 , predictive management method 1100 may includedetermining 1104 retail rate formulas for the plurality of retailelectric utility service providers. In an embodiment, determining 1100may include determining retail energy provider contracts and rateformulas as shown, for example, in FIGS. 61-71 Determining 1104 mayinclude accessing 1108 historical retail rates for the plurality ofretail electric utility service providers. Determining 1104 may includeanalyzing 1112 historical retail rates for the plurality of retailelectric utility service providers to determine a plurality of retailrate formulas for the plurality of retail electric utility serviceproviders. It will be understood that the determined retail rateformulas may be imputed or deduced retail rate formulas deduced byanalyzing 1112 historical retail rates for each one of the plurality ofretail electric utility service providers serving a geographic location.

Referring to FIG. 59 , method 1100 may include correlating 1116 each oneof the plurality of retail rate formulas to one or more correlationvariables. It will be understood, for example, that a first correlationvariable may be a weather or weather information variable. Correlationsor dependencies of the plurality of retail rate formulas may be providedor identified by such correlating 1116. A weather information variablemay include historical weather information records such as, for example,average daily high temperature or average daily low temperature.Examples of predicted weather information are shown in FIGS. 13-15 .FIGS. 77-80 show examples of real-world weather data observed from aspecific geographic location.

Referring to FIG. 59 , correlating 1116 may include correlating each oneof the plurality of retail rate formulas to one or more correlationvariables independent of weather. In embodiments for example, andwithout limitation, such correlation variables may include, time of yearor day of year variable, service provider behavior, the wholesale marketfor utility electric service, and third party information related toretail electric utility service providers. FIGS. 72-75 provide examplesof such third party data, in this case wholesale electricity prices fora specific geographic area in Texas. In an embodiment, correlating 116may include correlating weather or a weather information variable incombination with at least one or any of the following correlationvariables: time of year or day of year variable, service providerbehavior, the wholesale market for utility electric service, and thirdparty information related to retail electric utility service providers.

Referring to FIG. 59 , method 1100 may include first predicting 1120 thecorrelation variable. In an embodiment, wherein the correlation variableis weather information, method 1100 may include first predicting 1120 aweather prediction for the subject geographic location based on acorrelation function based upon weather information. Such weatherprediction may include predicting weather for, or as a function of, acalendar date or group of calendar dates, for the subject geographiclocation. In an embodiment, for example, first predicting 1120 mayinclude reference to a predictive weather model such as, for example, aweather model provided by the National Weather Service, National Oceanicand Atmospheric Administration (NOAA), or a commercial weatherprediction service such as Accuweather (available from Accuweather,Inc., State College, Pa.). Weather prediction may include for example,average daily high or average daily low.

Correlating 1116, discussed above, may include in addition to, orindependent of, retail market history, correlating one or moreadditional variables, such as wholesale market pricing history. In sucha case, comparable to the weather, a corresponding forecast orprediction (such as the one shown in FIG. 76 ), would be used for eachof the one or more additional variables. The one or more forecastedadditional variable may include a forecast generated from history forsuch additional variable, a forecast that may be obtained from a thirdparty, or both. In another example, FIG. 65 shows a retail energycontract that is tied directly to the pricing of natural gas. In orderto forecast the cost of retail energy contracts such shown in FIG. 65 ,a forecast of the natural gas market may be predicted, such as fromhistory of the natural gas market or from another predictive model forthe natural gas market, a forecast that may be available from a thirdparty, or both.

Referring to FIG. 59 , method 1100 may include second predicting 1124the market of retail rate formulas for the plurality of retail electricutility service providers for a period in relation to, or as a functionof, one or more correlation variables. In the particular embodimentshown in FIG. 1100 , method 1100 may include second predicting 1124 themarket of retail rate formulas for the plurality of retail electricutility service providers for a period in relation to, or as a functionof, the weather prediction for the period. Second predicting 1124 mayprovide the predicted market of retail rate formulas available from theplurality of retail electric utility service providers for a period.

Referring to FIG. 59 , method 1100 may include third predicting 1128consumer usage of electric utility service for a future period inrelation to, or as a function of, one or more correlation variables. Inthe particular embodiment shown in FIG. 1100 , method 1100 may includethird predicting 1128 consumer usage of electric utility service for afuture period in relation to, or as a function of, calendar date, timeand period. Third predicting 1128 consumer usage may include consideringor using 1136 consumer usage history for a previous period as apredictor for the future period. Usage history may be meter dataprovided from one or more smart meters operable for measuring usage ofelectricity for the facility. It will be understood that, inembodiments, such a smart meter may be a third party smart meter,installed by the facility occupant to provide a measurement and recordof electric utility usage independent of the meter, either smart orconventional, installed and maintained by the electric utility serviceprovider to determine billings to the facility occupant or owner. Wheresmart meter usage data is unavailable for the facility for a period,billing history may be utilized. Smart meter usage data ordinarily willbe available at higher resolution, such as for each hour or quarter-hourof a billing period, than is available from billing history. Forexample, as described above, FIG. 17 shows 15-min usage data observedfrom a smart meter over a 24 hour period, which may be considered inusing 1136. Where usage history is determined from billings for abilling period, such as a month, the usage history is not directlymeasured but instead is determined indirectly in relation to rateformulas applied by the utility service provider for the billing period.FIG. 16 shows billing history observed for a customer over a year, whichmay be used in using 1136. In an embodiment, third predicting 1128consumer usage also may include correlating 1140 consumer usage ofelectric utility service with weather. Third predicting 1128 consumerusage with weather may include correlating consumer usage withhistorical weather or a weather prediction for a period. Thirdpredicting 1128 consumer usage may include representing 1144 weatherdependent loads for the facility or residence. An exemplary mode forrepresenting and determining weather dependent loads is illustrated inFIGS. 18-21 , and was previously described above in relation to same.One of ordinary skill will understand that third predicting 1128 is notlimited to weather dependency or load disaggregation as disclosed forparticular embodiments, or to a particular manner of performing suchthird predicting. Third predicting 1128 consumer usage may includecollecting 1148 smart appliance usage data from the residence.Collecting 1148 smart appliance usage data may include, for example,collecting usage data for a smart thermostat or other smart appliance ornon-utility load monitoring device installed in the facility orresidence. FIG. 22 described above shows, for example, data collectedfrom a Nest smart thermostat over a 24 hour period, which may be used inthird predicting 1128.

Referring to FIG. 59 , method 1100 may include aggregating 1152predicted consumer usage of electric utility service for a future periodin relation to, or as a function of, calendar date, time and period.

Referring to FIG. 59 , method 1100 may include mapping 1156 a listing oftime-bounded predicted retail rate plan choices with predicted costs,for the both predicted retail market and the actual retail market incombination, including each predicted retail rate formula and eachactual retail rate formula for each retail utility service provider inthe market for the location of the facility or residence. The listing oftime-bounded predicted retail rate plan choices and actual retail rateplan choices in combination may include, or may be based upon, aprobabilistic model of retail rate plan choices with predicted costs, inaddition to the actual market of retail rate plan choices.

Referring to FIG. 59 , method 1100 may include second determining 1160the cost of each possible viable path, route, sequence or series(“paths”) of the plurality of time-bounded predicted retail rate planchoices, or decision nodes, considered each predicted retail rateformula for each retail utility service provider in the market for thelocation of the facility or residence for the evaluation period. Anexemplary representation of a plurality of time-bounded predicted retailrate plan choices is shown in FIG. 31 . Explanation of exemplaryapproaches for second determining 1160 is provided hereinabove at, forexample, paragraphs [0110]-[0114]. It will be understood by those ofordinary skill in the art, that one or more heuristics or heuristictechniques may be applied to eliminate unreasonable or clearly incorrectpaths, and thus avoid or reduce calculating the cost of paths that canbe eliminated by such heuristic approaches. Such possible, butunreasonable or clearly incorrect paths that may be eliminated byheuristics, may be referenced as non-viable paths. Possible paths, whichare not eliminated by heuristics, may be referenced as possible viablepaths. It will be understood that a large group of possible paths may bedivided into “non-viable” paths eliminated by heuristic techniqueswithout explicit calculation of cost for the path, and possible “viable”paths which remain after application of heuristic techniques haseliminated “non-viable” paths from consideration. The possible “viable”paths thus are not eliminated from consideration by heuristictechniques, such that explicit calculation of cost for the path isrequired, which is at least sufficient to eliminate that particular pathfrom consideration as being inferior to another possible “viable” pathacross the evaluation period.

Referring to FIG. 59 , method 1100 may include first selecting 1164 theleast cost path from the plurality of paths of the plurality oftime-bounded predicted retail rate plan choices, or sequences or pathsof decision nodes, across the evaluation period. An exemplaryrepresentation of a mode for first selecting 1164 the least cost pathacross the evaluation period is shown in FIG. 35 .

Referring again to FIG. 59 , method 1100 may include automated secondselecting 1168 of a retail utility service contract selection at adecision node and corresponding to the first selected least cost pathacross the evaluation period.

Referring to FIG. 59 , method 1100 may include automated switching 1172to a retail utility service contract selection by automated execution ofcontract documentation for the selection at the decision nodecorresponding to the first selected least cost path across theevaluation period. Automated switching 1172 may include automatedexecution of contract documentation by an agent execution system incommunication with a selected retail utility service provider. It willbe understood that method 1100 for selecting and receiving electricutility service may be iterative and repeated for a series of decisionnodes and contracted service periods.

Illustrated in FIG. 60 is a utility service predictive management system1200 for selecting and receiving retail electric utility service, in anexemplary embodiment. System 1200 may include a rate formula module 1222configured for determining service provider retail rate formulas fromhistorical rate data for retail utility service of each serviceprovider. In an embodiment, for example, rate formula module 1222 may beconfigured to perform the determining 1104 of method 1100, describedhereinabove and shown in FIG. 59 .

Referring to FIG. 60 , the utility service predictive management system1200 may include a correlation engine module 1226 configured forcorrelating service provider retail rate formulas with one or morevariables. In one embodiment, correlation engine module 1226 isconfigured for correlating service provider retail rate formulas with aweather variable. In an embodiment, for example, correlation enginemodule 1226 may be configured to perform the correlating 1116 of method1100, described hereinabove and shown in FIG. 59 . Referring again toFIG. 60 , it will be understood that, in embodiments, correlation enginemodule 1226 may be configured to correlate service provider rateformulas with any of the following variables: weather informationvariable, provider behavior, the wholesale market, and third partyinformation.

Referring to FIG. 60 , the utility service predictive management system1200 may include a variable prediction module 1230 configured forproviding a prediction of the variable, for each of the variablesdetermined by correlation engine module 1226. In an embodiment, thevariable prediction module 1230 may be, particularly, a weatherprediction module configured for providing a prediction of the weathervariable, for which correlation with the service provider retail rateformulas was determined by the correlation engine module 1226. In anembodiment, for example, the variable prediction module 1230 may be aweather prediction module configured to perform the first predicting1120 of weather for method 1100, described hereinabove and shown in FIG.59 .

Referring to FIG. 60 , the utility service predictive management system1200 may include a market predicting module 1234 configured forproviding a probabilistic retail rate plan prediction in relation to apredicted variable such as, for example, predicted weather or anotherpredicted variable. In an embodiment, for example, market predictingmodule 1234 may be configured to perform the second predicting 1124 ofmethod 1100, described hereinabove and shown in FIG. 59 .

Referring to FIG. 60 , the utility service predictive management system1200 may include a consumer usage predicting module 1238 configured forpredicting consumer usage in relation to one or more variables such as,for example, calendar date and weather. In an embodiment, for example,consumer usage predicting module 1238 may be configured to perform thethird predicting 1128 of method 1100, described hereinabove and shown inFIG. 59 .

Referring to FIG. 60 , the utility service predictive management system1200 may include a usage aggregating module 1242 configured forproviding an aggregate prediction of consumer usage for a period. In anembodiment, for example, usage aggregating module 1242 may be configuredto perform the aggregating 1152 of method 1100, described hereinaboveand shown in FIG. 59 .

Referring to FIG. 60 , the utility service predictive management system1200 may include a choices generating module 1246 configured forproviding a listing of time-bounded predicted rate plan choices, withcosts, for each predicted rate formula for an evaluation period. In anembodiment, for example, choices generating module 1246 may beconfigured to perform the generating 1156 of method 1100, describedhereinabove and shown in FIG. 59 .

Referring to FIG. 60 , the utility service predictive management system1200 may include a path determining module 1250 configured fordetermining the cost of each possible path of rate plan choices ordecision nodes across the evaluation period. In an embodiment, forexample, path determining module 1250 may be configured to perform thedetermining 1160 of method 1100, described hereinabove and shown in FIG.59 .

Referring to FIG. 60 , the utility service predictive management system1200 may include a path selecting module 1254 configured for comparingand selecting the least cost path from the plurality of rate planchoices or decision nodes across the evaluation period. In anembodiment, for example, path selecting module 1254 may be configured toperform the selecting 1164 of method 1100, described hereinabove andshown in FIG. 59 .

Referring to FIG. 60 , the utility service predictive management system1200 may include an automated service selecting module 1258 configuredfor performing automated selecting of service on a rate plan contractcorresponding to the selected least cost path across the evaluationperiod. In an embodiment, for example, automated service selectingmodule 1258 may be configured to perform the automated selecting 1168 ofmethod 1100, described hereinabove and shown in FIG. 59 .

Referring to FIG. 60 , the utility service predictive management system1200 may include an automated service switching module 1262 configuredfor performing automated switching of service for a residence to aretail utility service provider and rate plan contract corresponding tothe automated selecting of service for a choice or decision node. In anembodiment, for example, automated service switching module 1262 may beconfigured to perform the automated switching 1172 of method 1100,described hereinabove and shown in FIG. 59 .

Referring to FIG. 60 , the utility service predictive management system1200 may include a processor 1212 and memory 1214 combination. System1200 may include a communications bus 1218 for enabling operations ofthe modules. System 1200 may include a user interface 1264 for a user toinput data and receive prompts for interacting with system 1200.Examples of user interfaces in exemplary embodiments are shown in FIGS.11-12 and 36-44 System 1200 may include one or more smart appliances1266 in communication with processor 1212 for providing usage datacollected by the smart appliances 1266. Smart appliances 1266 mayinclude, for example, a smart thermostat or smart electric utilitymeter.

FIGS. 36-58 illustrate exemplary outputs via interfaces in exemplaryembodiments, such as system 1200.

FIGS. 61-71 illustrate exemplary retail energy provider contracts andretail rate formulas approximated in graphical form. As shown, theretail rate formulas may be of different types, different complexities,and different degrees of linearity or non-linearity, as a function ofusage, time of use, or both, in addition to different variables that maybe possible.

FIGS. 72-75 illustrate exemplary pricing on a wholesale energy market.The figures particularly depict both spot rates in real-time and averagerates for various periods of time in Texas.

FIG. 76 shows a wholesale electricity market forecast that may beaccessed in a method, or system, in exemplary embodiments. Theparticular forecast illustrated is provided by ERCOT for Texas from 2018to 2027, as of June 2018. The illustrated forecast projects peak demandin megawatts and energy usage in megawatt hours.

FIGS. 77-80 illustrate real-world weather data observed from a specificgeographic location.

Although the best methodologies have been particularly described in theforegoing disclosure, it is to be understood that such descriptions havebeen provided for purposes of illustration only, and that othervariations both in form and in detail can be made thereupon by thoseskilled in the art without departing from the spirit and scope thereof,which is defined first and foremost by the appended claims. Apparatus,methods and systems according to embodiments of the disclosure aredescribed. Although specific embodiments are illustrated and describedherein, it will be appreciated by those of ordinary skill in the artthat any arrangement which is calculated to achieve the same purposescan be substituted for the specific embodiments shown. This applicationis intended to cover any adaptations or variations of the embodimentsand disclosure. For example, although described in terminology and termscommon to the field of art, exemplary embodiments, systems, methods andapparatus described herein, one of ordinary skill in the art willappreciate that implementations can be made for other fields of art,systems, apparatus or methods that provide the required functions. Theinvention should therefore not be limited by the above describedembodiment, method, and examples, but by all embodiments and methodswithin the scope and spirit of the invention. In particular, one ofordinary skill in the art will readily appreciate that the names of themethods and apparatus are not intended to limit embodiments or thedisclosure. Furthermore, additional methods, steps, and apparatus can beadded to the components, functions can be rearranged among thecomponents, and new components to correspond to future enhancements andphysical devices used in embodiments can be introduced without departingfrom the scope of embodiments and the disclosure. One of skill in theart will readily recognize that embodiments are applicable to futuresystems, future apparatus, future methods, and different materials. Allmethods described herein can be performed in a suitable order unlessotherwise indicated herein or otherwise clearly contradicted by context.The use of any and all examples, or exemplary language (e.g., “suchas”), is intended merely to better illustrate the disclosure and doesnot pose a limitation on the scope of the disclosure unless otherwiseclaimed. No language in the specification should be construed asindicating any non-claimed element as essential to the practice of thedisclosure as used herein. Terminology used in the present disclosure isintended to include all environments and alternate technologies thatprovide the same functionality described herein.

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 24. Acomputer-implemented method for predictive management of retail electricutility service to a facility to select uninterrupted service for aperiod from a plurality of retail electric utility service providers tothe facility, said method comprising: first determining, by a processor,retail rate formulas for a market comprising a plurality of retailelectric utility service providers available to provide service to thefacility, by analyzing retail rate information for the plurality ofretail electric utility service providers; predicting, by a processor,retail rate formulas including retail rate plan choices for the marketof retail electric utility service providers for the period; predicting,by a processor, consumer usage of retail electric utility service forthe period; displaying, by a processor, a graphical representation of aplurality of uninterrupted cost paths from a plurality of predictedretail rate plan choices for the period; first selecting, by aprocessor, an uninterrupted least cost path from a plurality ofpredicted retail rate plan choices for the period; and performing, by aprocessor, one of the following: first receiving, by a processor, inputcomprising a retail utility service contract selection corresponding tothe first selected uninterrupted least cost path; and seconddetermining, by a processor, a retail utility service contract selectioncorresponding to the first selected least cost path.
 25. Thecomputer-implemented method of claim 24, further comprising: the firstselecting performed for each of a time series of decision nodes for theperiod.
 26. The computer-implemented method of claim 24, furthercomprising: switching, by a processor, to said retail utility servicecontract selection by automated execution of contract documentation byan agent execution system in communication with a selected retailelectric utility service provider, said automated execution authorizingsaid selected retail electric utility service provider to deliver retailelectric utility service to a meter at the facility.
 27. Thecomputer-implemented method of claim 24, further comprising: wherein thefirst selecting further comprises determining possible viable paths byapplying heuristics to reduce computation burden by eliminatingnon-viable paths from consideration.
 28. The computer-implemented methodof claim 24, further comprising: correlating, by a processor, each oneof the plurality of retail rate formulas to weather information; weatherpredicting, by a processor, a weather prediction for the facility forthe period; rate predicting, by a processor, retail rate formulas forthe market of retail electric utility service providers for the periodin relation to the weather prediction; usage predicting, by a processor,consumer usage of retail electric utility service for the period inrelation to the weather prediction.
 29. A system configured to performthe computer-implemented method of claim
 24. 30. A non-transitorycomputer readable medium including executable instructions which, whenexecuted by a computer, cause the computer to perform acomputer-implemented method according to claim 24.